

Find out in this report how the two Data Governance solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
If three engineers save ten hours each per month using erwin Data Modeler versus manual modeling, that equals three hundred sixty hours saved per year.
It replaces manual charting in Visio with a structured tool, providing significant return on investment.
If the modeling is compromised, then the entire structure will be compromised.
Engineering effort related to data integration and tracking maintenance decreased by roughly 40 to 50%.
If, for example, I made about 5,000 people who had already purchased not be targeted by advertising, I was saving a considerable amount of money that could be around €10,000 to €15,000.
It's hard to quantify exactly the hours spent, but from that abandoned basket flow, it was something around seven million DKK that has been made since we launched it.
The quality and speed of their support are excellent; everyone is very helpful, and they can solve problems quickly.
This rating reflects my ability to effectively utilize the tool and get support for licensing issues, installation errors, or corrupted repositories end-to-end.
I have had a positive experience with customer support; whenever we reach out to them, they are very responsive and helpful.
Segment's customer support is amazing.
At our initial phase, whenever I required any help related to the setup, I contacted them and they provided me with the solution in very little time.
I received my reply from Segment support team within 24 hours.
I would rate it probably a nine, making it a leader in data modeling.
erwin Data Modeler had a very good standardization infrastructure and supported a controlled multi-user environment with check-ins and check-outs.
Performance can degrade during larger collaborations and requires tuning for optimal performance.
As our user base, event volumes, and number of integrations increased over time, Segment was able to handle the additional load without requiring any major architectural changes from our side.
Segment's scalability can be measured in terms of transaction volume, which it handled very well for us in terms of millions of records.
It should be flexible and robust enough when it comes to handling different streams of data once the product is set up.
This lack of an auto-save methodology can be improved so that if a system crash occurs, work can be saved and rework can be avoided.
New versions often introduce enhanced features but may cause model crashes due to memory exhaustion.
Sometimes when I want to open the attribute editor, it stops working and the whole application freezes.
I have not experienced any critical outages directly impacting our business operations.
Segment is very stable with no major issues encountered.
The previous version of erwin Data Modeler used to crash unaccountably, but this one hasn't ever crashed on me, so it's been a lot more stable than the previous version that we had.
There is room for improvement in its AI capabilities, such as automatically suggesting normalization or identifying primary, foreign, or surrogate keys to make modeling more efficient.
There are many features, and I would expect good documentation detailing each feature, including when and how to use it, to be very useful because data modeling is not very popular in the data area and there aren't many educational videos regarding erwin Data Modeler.
More flexibility in pricing models or clear scaling options would make it easier for mid-sized companies to expand usage without significant budget concerns.
Ask the difficult questions to Segment about how the data will be used from a storage perspective and from a processing perspective.
Segment needs better integration with, or a much simpler integration with, external tools.
For a cloud or SaaS standard edition, it typically runs around two hundred to two hundred ninety-nine US dollars per month.
The experience with pricing, setup cost, and licensing is that the licensing process is painful and quite onerous.
It is more targeted toward an enterprise level since organizations looking to store business information and relationship values may consider the pricing.
revealing that Segment is much cheaper, particularly regarding premium licensing.
It is relatively cheap and very useful at that price point.
The cost is very high, but it is worth it.
One of the key aspects of data governance is defining the data dictionary and clearly identifying which data is accessible by whom and what is not accessible, particularly regarding PII-related data.
The way the data is organized and you have a visual of that organization helps a great deal in terms of trying to remember what you did and trying to retrieve the information.
Migrating DDLs using erwin Data Modeler is easy because I just connect to the database and generate the data model from what is already implemented, making the process straightforward.
I think predictive audiences require a 360 view of the customer in order to create these types of AI audiences because they use a lot of different data points.
Segment has improved our data quality and our ease of collection, and most importantly, it has saved us time by not having to maintain a custom tool for server-side tracking.
Segment has positively impacted my organization by reducing implementation time to one-third of what it previously took.
| Product | Mindshare (%) |
|---|---|
| erwin Data Modeler | 0.5% |
| Segment | 0.7% |
| Other | 98.8% |


| Company Size | Count |
|---|---|
| Small Business | 17 |
| Midsize Enterprise | 4 |
| Large Enterprise | 43 |
| Company Size | Count |
|---|---|
| Small Business | 7 |
| Midsize Enterprise | 1 |
| Large Enterprise | 6 |
Erwin Data Modeler provides an effective approach to visualizing and managing data models. It assists in creating, reversing, and synchronizing data models with ease, supporting logical and physical transitions while enhancing understanding across teams.
Erwin Data Modeler is a comprehensive tool designed for professional database management. It offers capabilities to organize and enforce standards, automating script generation with robust reverse engineering and DDL output. Users can manage complex data environments, capitalize on integration with data intelligence, and maintain large-scale databases smoothly. Despite its strengths, improvements in multi-language support, database integration, and reporting features are needed. Users benefit from extensive support for conceptual, logical, and physical database modeling, enhancing architectural design and data governance for platforms like SQL Server, Oracle, and Teradata.
What are the key features of Erwin Data Modeler?Erwin Data Modeler finds application in industries focused on robust data management, implementing it for enterprise data warehouses, business domain models, and operational systems. It supports architectural design and governance, aligning with business applications demanding precise data representation and visualization.
Segment offers dynamic audience-building and precise tracking, enhancing marketing efficiency and simplifying data management for businesses. It integrates well with analytics services, promoting effective customer insights and personalized campaign executions.
Segment provides businesses with sophisticated tools that streamline workflows and improve campaign efficiency. With user-friendly implementation, it significantly enhances marketing outcomes by simplifying data management through seamless integration with analytics services. Its unified profile system facilitates comprehensive customer insights, while reducing integration efforts via built-in links to diverse data sources. Businesses benefit from highly accurate website tracking and powerful predictive audience tools, ensuring that their marketing strategies effectively target main audiences and achieve better personalization.
What are the key features of Segment?Industries leverage Segment for its ability to serve as a central data hub, effectively managing CRM integrations and audience targeting. Companies utilize it to connect databases for tracking and analyzing server-side user actions. Retail businesses employ it for journey building and cart abandonment tracking, while tech firms optimize sales funnels through strategic data management, leading to more effective lead qualification and conversion processes.
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